Unlocking genome-based prediction and selection in conifers: the key role of within-family prediction accuracy illustrated in maritime pine (Pinus pinaster Ait.)
SANCHEZ, Leopoldo
Biologie intégrée pour la valorisation de la diversité des Arbres et de la Forêt [BioForA]
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Biologie intégrée pour la valorisation de la diversité des Arbres et de la Forêt [BioForA]
Langue
en
Article de revue
Ce document a été publié dans
Annals of Forest Science. 2024-12-30, vol. 81, n° 1, p. 52
Springer Nature (since 2011)/EDP Science (until 2010)
Résumé en anglais
<div><p>Key message Based on experimental and simulated data for maritime pine (Pinus pinaster Ait.) in a genomic selection context, our study reveals that the often-highlighted equivalence between genome-based and ...Lire la suite >
<div><p>Key message Based on experimental and simulated data for maritime pine (Pinus pinaster Ait.) in a genomic selection context, our study reveals that the often-highlighted equivalence between genome-based and pedigree-based prediction accuracies of breeding values is associated with a zero accuracy of genome-based prediction within families, which can be attributed to the still insufficient size of the genomic training sets for conifers.</p><p>Context Genomic selection is a promising approach for forest tree breeding. However, its advantage in terms of prediction accuracy over conventional pedigree-based methods is unclear and within-family accuracy is rarely assessed.</p></div> <div>Aims<p>We used a pedigree-based model (ABLUP) with corrected pedigree data as a baseline reference for assessing the prediction accuracy of genome-based model (GBLUP) at the global and within-family levels in maritime pine (Pinus pinaster Ait).</p></div> <div>Methods<p>We considered 39 full-sib families, each comprising 10 to 40 individuals, to constitute an experimental population of 833 individuals. A stochastic simulation model was also developed to explore other scenarios of heritability, training set size, and marker density.</p></div> <div>Results<p>Prediction accuracies with GBLUP and ABLUP were similar, and within-family accuracy with GBLUP was on average zero with large variation between families. Simulations revealed that the number of individuals in the training set was the principal factor limiting GBLUP accuracy in our study and likely in many forest tree breeding programmes. Accurate within-family prediction is possible if 40-65 individuals per full-sib family are included in the genomic training set, from a total of 1600-2000 individuals in the training set.</p></div> <div>Conclusions<p>The increase in the number of individuals per family in the training set lead to a significant advantage of GBLUP over ABLUP in terms of prediction accuracy and more clearly justify the switch to genome-based prediction and selection in forest trees.</p></div>< Réduire
Mots clés en anglais
Breeding programme
Genomic selection
Maritime pine
Progeny validation
Stochastic simulation
Within-family variability
Origine
Importé de halUnités de recherche